Lab success doesn’t guarantee commercial success. This is where pilot plants come into play. They help manufacturers move from concept to reality while minimizing technical and financial risks inherent to scale-up. For chemical operators, pilot plants validate reaction kinetics, characterize scale-dependent behavior, optimize processing conditions, and identify operational challenges before fully committing to the technology.

But as pressure grows to compress commercialization timelines, organizations are rethinking how they design and operate pilot plants. Rather than relying only on traditional large-scale pilot programs, manufacturers are implementing strategies such as digital twins, modular chemical process intensification (MCPI), advanced process control and minimum viable pilot plant (MVPP) methodologies.

These strategies are more targeted, flexible and data-driven than previous pilot projects. Proper sizing is equally important because oversized pilots can introduce unnecessary cost and complexity, while undersized systems may fail to capture the scale-dependent phenomena that determine commercial viability.

Next-generation pilot-plant strategies combine physical experimentation with digital technologies, such as smart sensors, process analytical technologies, advanced analytics, artificial intelligence, machine learning and digital twins to improve process visibility, accelerate learning cycles and strengthen decision-making.

Emerging methodologies like MCPI and MVPP also advance pilot initiatives by enabling more focused experimentation and more efficient development pathways.

When combined with digital twins, process analytical technologies, advanced analytics and AI capabilities, these technologies facilitate smarter experimentation and more effective knowledge transfer. Pilot testing remains a non-negotiable commercialization gate, particularly when significant financial risk is involved, but new technologies continue to reshape how chemical manufacturers approach development and scale-up.

Why Companies Invest in Pilot Plants

Many technologies that pass lab tests don’t advance to industrial deployment. Other projects take years or decades to demonstrate commercial application. This transition period between lab success and commercial validation is sometimes known as the “valley of death."

A common approach to crossing that valley is the gradual deployment of reduced-scale technologies in settings shielded from market competition. When implemented in process industries or the energy sector, these infrastructures become pilot and demonstration plants.

Technical Readiness Levels (TRLs) provide a framework for estimating how close a design is to reliable full-scale operation. Historically, building a safe and cost-effective full-scale commercial plant has involved progressing from TRL 2 or 3 to TRL 5 before larger-scale deployment occurs.

Proper Facility Sizing is Crucial to Success

Pilot plants often cost millions of dollars to build and operate and the failure rate of technology scale-up is high due to technical risks, uncertain end-product markets and funding failures.

Although technological advances make designing and implementing pilot plants easier, determining optimal pilot plant size remains a barrier to success. Typically, pilot plants represent only a small fraction of a commercial plant’s production capacity. The goal is to build systems large enough to capture critical scale-dependent phenomena without the high cost and complexity.

In polymer processing, pilot-plant capacities range from 0.1%-10% of full-scale throughput, although exact scaling factors vary by process and objectives. In most cases, capacities fall within the range of 0.1%-1% of industrial scale.

Methodologies for determining pilot scale rely on engineering similarity principles, including geometric scaling and dimensionless numbers, such as the Reynolds number for mixing and the Prandtl and Nusselt numbers for heat transfer.

But in chemical processes (such as non-Newtonian fluids), physical-model material systems can’t fully replicate physical properties. Engineers then rely on “rules of thumb” for different types of equipment or situations. The drawback to this approach is that they may only achieve partial similarity.  

Using these values and methods, companies can maintain key scale-up ratios, such as volume-related mixing power (P/V) for mixing vessels and superficial velocity (VG) for bubble columns, helping to more accurately replicate the behavior of the future full-scale unit.

It’s also vital for engineers to determine which process parameters must remain constant during scale-up. These variables may include temperature, pressure, reaction time (residence time) and agitation speed profile while accounting for more complex factors such as heat removal requirements and mixing energy inputs.

De-Risking New Technologies For Success

Another common challenge for companies implementing pilot plants is de-risking new technologies. The traditional pathway for de-risking is: 1) bench-scale testing, 2) pilot programs, 3) demonstration plants and 4) commercial plant construction as technologies progress through TRLs. Historically, hastening or eliminating any of these steps increased risk exposure for developers and investors. Unfortunately, this pathway requires significant time and financial investment, creating pressure to identify strategies that reduce development timelines without increasing commercialization risk.

MCPI, advanced process control and MVPP methodologies support that objective. The original MCPI concept started in the 1970s from Imperial Chemical Industries as a manufacturing philosophy focused on reducing equipment size, waste generation and energy consumption. The modular aspect was added later as the technology became available.

Unlike conventional scale-up approaches that depend on larger facilities, MVPP is a more systematic, capital-efficient framework. By adapting the proven principles of the lean startup methodology and, crucially, by formalizing a pattern of successful de-risking observed throughout a century of chemical engineering history, the MVPP reframes the goal of piloting from demonstration to targeted, hypothesis-driven learning.

How Digital Twins Improve Pilot Plant Performance

Engineers can use digital twins to identify operational bottlenecks before physical construction, simulate process behavior under varying conditions, optimize heat transfer and fluid dynamics and reduce pilot campaign durations through more efficient testing approaches.

In chemical processing, digital twins comprise three core layers that work together to create a real-time representation of plant operation:

• Physical layer. Equipment and systems being modeled, including reactors, distillation columns, furnaces, pumps and other process assets.
• Digital model layer. A virtual representation that simulates process conditions, equipment behavior and system interactions.
• Continuous data feedback layer. Real-time operational data continuously updates digital twins to reflect real-time operating conditions and improve prediction accuracy.

Early adopters of digital twins have demonstrated the potential to streamline operations, optimize chemical reactions and improve product quality and performance. Organizations also use these systems to simulate fluid dynamics and heat transfer behavior, enhance safety planning, improve emergency response and reduce emissions.

Within process development and pilot plants, digital twins accelerate design and commissioning phases by enabling rapid prototyping and scenario simulation. These capabilities enable the identification of design flaws and shorten the time to market for new processes.

With digital twins, engineers can perform rigorous sensitivity analyses to identify the critical unit operations carrying the highest technical uncertainty or the greatest economic impact. Combined with MCPI and MVPP methodologies, digital twins help manufacturers prioritize certain experimentation strategies over others, reduce development risk and improve commercialization efficiency.

High-fidelity digital-twin systems can support solvent recovery optimization, byproduct utilization studies, predictive maintenance planning and product quality improvements. As pilot strategies increasingly combine physical experimentation with digital capabilities, simulation can help accelerate commercialization while improving operational decision-making.

Pilot Plants Bridge the Gap

Most companies will not rely entirely on simplified concepts and digital twins for step-out innovations because the scale-up risks and capital expenditures required to develop new processes for industrial plants are substantial, and chemical systems are notoriously difficult to predict and fully characterize. Full-scale pilot plants are necessary to demonstrate complex system-level interactions, including trace impurity accumulation and the dynamic response of integrated processes.

They also facilitate innovation, catalyst optimization, process improvement, waste reduction, safety validation and customer proof-of-performance activities.

The future of pilot-plant development will likely depend on the effective combination of physical testing and digital tools. Manufacturers that successfully integrate experimentation, simulation, and connected process technologies can improve commercialization outcomes while adapting to future technical and market challenges.